Data analysis method for clarification of perceptions

ABSTRACT

A method of and apparatus for analyzing thoughts, perceptions, knowledge, feelings etc., of an individual. A set of elements are input or selected by a user. A single element and a pair of elements are formed. A user inputs similar characteristics between the pair of elements and different characteristics between the single element and pair of elements. This is performed for a number of iterations and elements and characteristic combinations. The elements are then ranked by a user in relation to each characteristic and the rankings are analyzed to determine the correlation between elements and characteristics. The analysis may be expanded or refined and further elements and characteristics may be added at any stage.

THE TECHNICAL FIELD

This invention relates to a method of analysis and an apparatus forimplementing the method. More particularly, but not exclusively, thepresent invention relates to an analysis tool for exploring thethoughts, perceptions, knowledge and feelings of an individual.

The present invention provides an open and flexible tool having wideranging potential applications. The present invention may findapplication in education, commerce, self analysis, entertainment, marketresearch, expert systems, interviewing, designing organisationalcompetencies, bench marking cultures, developing personnelspecifications etc.

BACKGROUND OF THE INVENTION

To date a variety of techniques have been used which attempt to use theunderlying principles for counselling and to research areas in whichcounselling is required. Computer implemented systems have been producedwhere the results of a consultation session may be input into a computerand processed to highlight strong correlations between data (be itpeople, concepts, emotions, ideas etc). This approach is limited in thatan experienced interviewer is required to interview the subject in orderto obtain the data to be processed. Further, there is typically a singleiteration of the programme run to highlight the areas in whichcounselling is required. The results are therefore not as refined asthey would be if a number of iterations could be performed.

DISCLOSURE OF THE INVENTION

It is an object of the present invention to provide an interactiveanalysis method and apparatus which enables a user to explore theirthoughts, perceptions and feelings etc in a desired area withoutrequiring input from a professional interviewer, or to at least providethe public with a useful choice.

According to a first aspect of the invention there is provided anapparatus for analysing data comprising:

means for inputting or selecting a plurality of data elements accordingto user command;

means for grouping data elements into groups;

means for communicating the elements of the groups to a user;

means for inputting or selecting characteristics within and/or betweendata element groups according to user command;

means responsive to user command for ranking data elements in relationto selected characteristics; and

means for comparing rankings between elements and/or characteristics andfor determining the elements and/or characteristics having selecteddegrees of correlation including means for selecting a user definedcorrelation threshold and displaying those elements or characteristicsabove or below the correlation threshold.

According to a further aspect of the invention there is provided acomputer controlled method of analysing data comprising:

inputting a plurality of data elements into a data processor orselecting a plurality of elements stored in memory of the dataprocessor;

actuating the data processor to group the data elements into groups;

inputting or selecting characteristics within or between the elements;

inputting ranking information to the data processor to rank the dataelements in relation to the characteristics;

processing rankings between elements and/or characteristics in the dataprocessor to determine elements and/or characteristics having selectedlevels of correlation;

inputting a correlation threshold; and

displaying those elements or characteristics having a degree ofcorrelation above or below the correlation threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described by way of example with reference tothe accompanying drawings in which:

FIG. 1 shows a flow diagram illustrating the main processing steps;

FIG. 2 shows a session set up screen;

FIG. 3 shows an element entry screen;

FIG. 4 shows a screen for adding a construct;

FIG. 5 shows a screen for selecting an element;

FIG. 6 shows a screen for adding a construct;

FIG. 7 shows a laddering up strategy screen according to a firststrategy;

FIG. 8 shows the screen shown in FIG. 7 after data has been entered inthe text box;

FIG. 9 shows a laddering up screen implementing a second strategy;

FIG. 10 shows a laddering down screen;

FIG. 11 shows a rating screen;

FIG. 12 shows a screen for facilitating the rewriting of constructs;

FIG. 13 shows a graphical representation of degrees of correlationbetween elements and constructs;

FIG. 14 shows an element and construct differentiation screen;

FIG. 15 shows an element differentiation screen for displaying elementshaving a degree of correlation higher than a set threshold;

FIG. 16 shows a screen for adding a construct;

FIG. 17 shows a screen for identifying constructs having a correlationgreater than a set threshold;

FIG. 18 shows a screen for entering new elements to better differentiatehighly correlated constructs; and

FIG. 19 shows an additional element entry screen.

BEST MODE FOR CARRYING OUT THE INVENTION

The apparatus is preferably a data processing means, such as a personalcomputer, having a display and keyboard and/or mouse. FIG. 1 is a flowdiagram showing the general structure of the program run by the dataprocessing means. This is a simple diagram to assist understanding andit will be appreciated from the following that the process isinteractive and that new data may be entered, existing data modified andprocessing may occur in any desired sequence.

A user may input selected data elements or select from stored dataelements. Alternatively, the apparatus may prompt a user to assist inthe creation of elements. Qualifiers can either be input by a user orselected from a set of stored qualifiers.

The elements are then sequentially grouped in groups of three elementscomprising a pair of elements and a singleton. The pair of elements ispresented to a user and the user is asked to specify how the elementsare similar in terms of a selected qualifier. The user is then asked howthe singleton differs from the pair of elements. This is donesequentially for a variety of element groupings and qualifiers. A numberof characteristics or “constructs” are developed by thisprocess—constructs may comprise a pair of contrasting characteristicsdefining two opposing poles.

To further refine the characteristics a user may be prompted togeneralize or abstract a characteristic or identify a more specificcharacteristic. Upon selection, one of the refinement options may appearwhich questions a user to input a more generalized or specificcharacteristic.

A measurement range may then be input to define the range within whicheach element is to be ranked in accordance with a given characteristic.A user then enters a value within the range for each characteristic inrelation to each element. A matrix of values is formed with the dataelements and characteristics forming the axes of the matrix.

The apparatus then compares all rows and columns to find those rowswhich are most closely correlated. The most closely correlated rows andcolumns are combined to form new composite rows and columns. Thecomposite rows and columns are then compared with the remaining rows andcolumns to determine the next most correlated rows and columns and so onuntil only two rows and columns remain.

The apparatus computes the degree of correlation between data elementsand between characteristics. The results of this analysis may be showndendritically. Different portions may be different colours to indicatethe different degrees of correlation. The user may set a correlationthreshold and the device will identify pairs of elements orcharacteristics above or below the correlation threshold.

Where highly correlated elements or characteristics are located, a usermay choose to differentiate between the pair of elements orcharacteristics if a user believes that they should in fact bedifferentiated. The new characteristic entered by a user may then beranked against all elements and a new matrix formed. Alternatively, thepair of elements or characteristics may be condensed into a singleelement or characteristic.

The apparatus allows a new element or characteristic to be entered atany stage, for ratings to be conducted against all elements andcharacteristics and for a new matrix to be calculated. This interactiveprocess enables a highly refined model to be developed. Further, onemodel can be compared with another model to compare the correlationbetween models.

The following embodiment describes the operation of a computer programoperating in a Windows™ environment running on a PC and implementing themethod of the invention.

Upon starting the program a development screen appears followed by amain menu. An existing session can be loaded by opening a file or a newsession initiated. Once a session is started the next step is to entersession setup parameters. When the “session setup” button is selected awindow as shown in FIG. 2 is shown. By clicking on the “purpose” buttona purpose can be selected from a selection of purpose files.Alternatively, a defined purpose may be entered. The purpose reflectsthe reason a user has chosen to conduct a particular session.

A user may select an element class by selecting the “element class”button. Again, the user can select an element class from a storedselection or enter a user defined element class.

A user may enter the elements for the session in a number of ways. Theelements should be concrete, discrete and homogeneous and cover a goodrange of possible options. Elements must be of the same class. Byclicking the “element question” button the screen shown in FIG. 3appears which prompts a user with questions to help a user selectelements for a session. The “next” and “prev” buttons cause the next orprevious questions to be displayed “delete” deletes an entered element.“OK” may be selected to accept the elements and exit whereas “cancel”simply exits the screen. A user can enter desired elements as prompted.

Upon selecting the “elements” button the user can select a set of storedelements or enter desired elements. Likewise, upon selecting the“qualifiers” button a user can select pre-existing qualifiers or adduser defined qualifiers. Qualifiers are used to channel the process inthe desired way.

Once the parameters for the session are set the user clicks the “okay”button. Online help is provided in any dialogue by selecting the “help”button.

Once the parameters have been set up development of a model cancommence. A user may then select an “add construct” option from the mainscreen to proceed. The dialogue window that appears is shown in FIG. 4.This window shows three elements: Ronald Reagan, Winston Churchill andMargaret Thatcher. The user is asked to state something that twoelements have in common (Ronald Reagan and Winston Churchill) andsomething that makes the third (Margaret Thatcher) different from theother two in terms of the qualifier (how I feel about them). The user isprompted to enter in the first box how Ronald Reagan and WinstonChurchill are similar. The user types in the similarity and moves to thesecond box to enter how Margaret Thatcher is different. This is theprocess of defining “constructs” comprising two contrasting poles. Theseconstructs as stored as the construct creation process progresses.

Once the first construct has been entered the user selects the“continue” button. The user is then prompted to add two more constructsin the same way for the same elements and qualifier. This continuesuntil the user can think of no more constructs. The user may then selectthe “select elements” button to bring up the entire element set and awindow as shown in FIG. 5. A user may then select the desired threeelements by moving the cursor and clicking a mouse and, once three areselected, clicking on the “okay” button. In this way a user can definethe three elements used to develop constructs.

Alternatively, a user may select the “new element set” button (FIG. 4)in order for the computer to automatically select a new set of threeelements. The user will then be presented with a new set of elements(Richard Nixon, Bill Clinton and Queen Elizabeth II) as shown in FIG. 6.Constructs can be entered in the same way for these elements in terms ofthe selected qualifier.

By selecting the “change qualifier” button (FIG. 4), the qualifierapplied to the three elements can be varied. For example, “how I feelabout them” may be changed to “their impact as leaders”. Selecting the“re-order elements” button regroups the three elements into a different2,1 grouping. This process may continue until a sufficient number ofconstructs have been formed. At this point a user can click the “okay”button to move onto the next stage.

To further refine constructs “laddering up” or “laddering down” optionsmay be used to create more generalized or specific constructs. If the“laddering up strategy 1” is selected a window as shown in FIG. 7 willappear. The user is asked to specify the important distinction betweenthe constructs “accused of corruption” and “strictly incorruptible”. Theuser may then enter their answer in the box “reasons why” (see FIG. 8).Constructs can be scrolled through by selecting the “next” or “prev”buttons. If the “next level” button is selected the user is asked tofurther define the reason given in the previous response. Usually thiswill be limited to three levels of refinement. Once the desiredrefinement has been achieved the “okay” button may be selected.

If the “laddering up: strategy 2” is selected a window as shown in FIG.9 will appear. This presents the information in a slightly differentmanner. The user is asked to select which of the bipolar extremes of theconstruct he or she prefers. The preferred construct is selected byclicking on the button adjacent the construct to be selected (e.g.strictly incorruptible). The user is then asked to identify why one poleor the other is preferred. In this way, the user is also driven todetermine what is the core reason for their preference.

Alternatively, the user may wish to develop more specific constructs.For example, to evaluate a persons performance a user may want to focusupon what aspect of performance is to be compared. In this case a“laddering down” approach may be adopted. Upon selecting “ladderingdown” from the menu the “laddering down” dialogue button shown in FIG.10 appears, This asks a user to give examples of either pole of aconstruct. By selecting the “change prompt” button a user can change thestructure of the question at the top of the window. By selecting the“change qualifier” button the user can change the qualifier use in thequestion (i.e. how I feel about them). The user can move betweenconstructs by selecting the “next” or “previous” buttons. Using theladdering down process, more specific constructs can be produced.

Once a user is happy with constructs the next step is to rate elementsin relation to each construct. Upon selecting the rating option a windowas shown in FIG. 11 appears. The user must first set the rating range tobe used in the rating process. This may be achieved by and clicking onthe button “set rating range” and selecting the number at the top end ofthe range. A user may then rate the first construct for each element byclicking on the diamond shaped box adjacent each number at the bottomleft of the screen for each element. For example, the first element“Bill Clinton” is highlighted and clicking on the button adjacent thenumber 3 will rate Bill Clinton with the number 3. The bar will thenmove down to the next element to be rated.

Once all of the elements are rated a user may select the “next” buttonto rate the elements in relation to the next construct in a similarmanner. To go back to a previous construct the “prev” button may beselected. The “rewrite” button enables a user to rewrite a construct.Upon selecting the “rewrite” button a screen as shown in FIG. 12 appearsand a user can change the constructs as desired. Once the desiredconstructs are entered the “OK” button may be selected. Once theelements are rated the “okay” button may be selected. The “cancel”button terminates the rating.

Once the elements have been rated the program develops a matrix of theratings for each element in relation to each construct (see FIG. 13).The top row and right hand side column correspond to the elements andconstructs respectively. All columns are compared to determine the mostclosely correlated columns. Correlation involves comparing each columnto each other column. There are nine columns in the example shown. Thetwo most closely correlated columns are then combined to form a newcomposite column or node 10. This node 10 is then compared to allremaining columns in the same manner. The next two most closelycorrelated columns are 2 and 5 and the new node 11 is created as acombination of both. This process continues until all columns have beencondensed into the two nodes 16 and 17. The rows are processed in likefashion together with their inverses.

The axes 100, 90, 80, 70 indicate the degree of correlation between.rows and columns. These relationships are analysed dendritically. Thisenables a user to visually determine the degree of correlation betweenelements and constructs as shown in FIG. 13. Such a graphicalrepresentation may be shown on user request. The degree of correlationbetween rows may also be indicated using colour. A user may point to anyparticular element or construct and click on it to reveal the identityof the element or construct, or to select it for furtherdifferentiation.

To produce the dendritic diagram shown in FIG. 11 the nodes must bearranged to produce an arrangement in which connecting lines do notcross. To do this the nodes (13 and 16 for the columns of FIG. 13 )forming the highest numbered node (e.g. 17) are firstly considered. Thehighest numbered node (16) of the nodes below node 17 is placed to theright of node 17 and the other node is placed to the left. Likewise node12 is placed to the right and node 8 to the left of node 13. Thisprocedure is carried out until all nodes are arranged in this manner.The rows and columns are then arranged in the matrix according to thedetermined order and the nodes placed according to their correlationlevels (e.g. 80, 90, 100 etc) and joined by lines showing the linkagesbetween nodes.

The next step is to differentiate elements and constructs. Uponselecting the differentiation option from the main menu a dialoguescreen as shown in FIG. 14 will appear. Either an element or constructmay be selected in the first box. Either discrete or non-discreteelements may be selected. In the lower box a user can select thecorrelation level to be applied to identify pairs of constructs orelements above a predetermined threshold. Once the correlation level isselected by moving the bar, the “okay” button may be selected toproceed.

Next a window as shown in FIG. 15 will appear. The window will identifyto a user those elements or constructs that are closely correlated andask whether it is true that the elements or constructs are very similar.If a user selects the “yes” button the next set of closely correlatedelements is shown. If the user selects the “no” button, a window asshown in FIG. 16 will appear asking the user to enter constructsidentifying the differences between the two elements or constructs. Theuser then enters the two poles of the construct within the windowsadjacent the elements or constructs shown (i.e.

John F. Kennedy and Winston Churchill). Having redefined the constructthe user may then re-rate the elements in relation to the new constructby selecting the “rate elements” button. Once the elements have beenrated against the new construct (as previously described) and the “okay”button is selected the user is returned back to the window as shown inFIG. 16. By selecting the “okay” button the user is returned to the mainmenu.

A user can sequentially go through the differentiation process selectingdifferent levels of correlation to incrementally change the model.

When differentiation of a construct is selected from the window in FIG.14 a window as shown in FIG. 17 appears. The user is given the option ofsupplying a construct which combines the meaning of the two similarconstructs, entering a new element which will better differentiate theconstructs or leaving the model as it is. Upon clicking on the button tooffer a fresh construct and selecting “okay” a user can enter aconstruct to replace the previous construct. Upon selecting the “RateElements” button the elements can be re-rated.

Upon selecting the option to enter a new element and selecting “okay”the window shown in FIG. 18 appears. A user may scroll through existingelements and enter a new element from a range of options or type in anew element. Upon selecting the “rate elements” option the user canre-rate the new elements in relation to the constructs as before. Oncecompleted the user clicks on the “okay” button.

The program archives elements and constructs as they are created. Asession (i.e. elements and constructs that have been rated and analysed)may be saved at any stage so that a user may return to a desired pointof development.

There is also a facility to mark elements or constructs so that onlyselected elements or constructs are displayed. Elements or constructsmay also be prioritised (e.g. high, medium, or low) so that differentpriorities or groups of priorities may be selectively displayed.

It will be appreciated that further elements or constructs can be addedor deleted at any stage (i.e. by selecting an “add element” option fromthe main menu the screen shown in FIG. 19 appears to allow entry of newelements), the elements re-rated against each construct and a new modelgenerated and graphically displayed by the program. The interactivenature of the program and the prompting of the computer enables a highlyrefined model to be produced by a user in private without requiring theintervention of a counsellor. The method and apparatus removesinterviewer bias and enables a content specific exploration within auser's own framework. There is also the facility to readily change themodel and compare it with others. When compared matrices have the sameelements and constructs a direct measure of correlation between matricescan be calculated. When only elements are common the comparison betweenmatrices may be useful to identify areas for discussion etc.

It is to be appreciated that the invention may be implemented in anumber of ways and that the following description is given purely by wayof example. For example it is to be appreciated that a visual display isnot required and that the apparatus could output audio information andinclude speech recognition software to respond to voice comments.

Where in the foregoing description reference has been made to integersor components having known equivalents then such equivalents are hereinincorporated as if individually set forth.

Although this invention has been described by way of example it is to beappreciated that improvements and/or modifications may be made theretowithout departing from the scope of the present invention as set out inthe appended claims.

What we claim is:
 1. An apparatus for analysing data comprising: meansfor inputting or selecting a plurality of data elements according touser command; means for grouping data elements into a singleton and apair of elements; means for communicating the elements of the groups toa user; means for inputting or selecting a common characteristic betweenthe pair of elements and a difference characteristic between the pair ofelements and the singleton, means responsive to user command for rankingdata elements in relation to selected characteristics; and means forcomparing rankings between elements and/or characteristics and fordetermining the elements and/or characteristics having selected degreesof correlation including means for selecting a user defined correlationthreshold and displaying those elements or characteristics above orbelow the correlation threshold.
 2. An apparatus as claimed in claim 1wherein the data elements may be inputted by a user or selected from apre-existing selection of data elements stored in the apparatus.
 3. Anapparatus as claimed in claim 1 wherein the user may select an automatedelement creation option in which the apparatus produces questions toassist a user in creating a set of elements.
 4. An apparatus as claimedin claim 1 wherein a user is prompted to input the common and differencecharacteristics in relation to a defined qualifier.
 5. An apparatus asclaimed in claim 4 wherein a plurality of qualifiers may be input by auser or selected from a selection of stored qualifiers.
 6. An apparatusas claimed in claim 1 wherein the apparatus prompts a user to providecommon and difference characteristics for a plurality of elementgroupings.
 7. An apparatus as claimed claim 1 wherein a user can selectthe elements to be compared.
 8. An apparatus as claimed in claim 1wherein a user may reorder selected data elements into differentgroupings.
 9. An apparatus as claimed in claim 1 wherein thecharacteristics input by a user are stored by the apparatus.
 10. Anapparatus as claimed in claim 1 wherein, upon user selection, a windowprompts the user to generalize a selected characteristic and store a newcharacteristic.
 11. An apparatus as claimed in claim 1 wherein, uponuser actuation, a window appears to prompt a user to more specificallydefine a characteristic and store a new characteristic.
 12. An apparatusas claimed in claim 1 wherein the means for ranking data elementsincludes means responsive to user input for defining a measurementrange.
 13. An apparatus as claimed in claim 12 wherein a user may entera value within the measurement range for each characteristic in relationto each data element.
 14. An apparatus as claimed in claim 13 whereinthe apparatus forms a matrix having the data elements along one axis,the characteristics along the other and the values entered forming thematrix.
 15. An apparatus as claimed in claim 14 wherein the apparatuscompares the values for each column of elements and/or each row ofcharacteristics to find the closest correlation, forms a new columnand/or row combining the most closely correlated rows and/or columnsrespectively and continues to compare rows and/or columns in this manneruntil only two rows and/or columns are left.
 16. An apparatus as claimedin claim 15 wherein the apparatus stores figures representative of thecorrelation between rows and columns of the matrixes.
 17. An apparatusas claimed in claim 15 wherein the correlation between columns and rowsis shown graphically on a display device.
 18. An apparatus as claimed inclaim 17 wherein the rows and/or columns are arranged by iterativelycomparing the two rows and/or columns forming a selected row and/orcolumn and placing the most recently formed row and/or column to theright of the selected row and/or column and the other row and/or columnto the left.
 19. An apparatus as claimed in claim 17 wherein thecorrelation between columns and rows is shown dendritically.
 20. Anapparatus as claimed in claim 17 wherein color is used to indicatecolumns or rows having levels of correlation within predefined ranges.21. An apparatus as claimed in claim 1 including differentiation meanswhich displays elements or characteristics having a required degree ofcorrelation to prompt a user to review the elements or characteristics.22. An apparatus as claimed in claim 21 wherein a user has the option ofcombining any pair of closely correlated elements or characteristics.23. An apparatus as claimed in claim 21 wherein a user may enter a newcharacteristic to define a difference between two correlated elements orcharacteristics.
 24. An apparatus as claimed in claim 23 wherein allelements may be re-rated in respect of the new characteristic toconstruct a new matrix.
 25. An apparatus as claimed in claim 1 wherein anew characteristic may be added at any stage, ranked in relation to eachelement and processed to form a new matrix.
 26. An apparatus as claimedin claim 1 wherein the apparatus includes checking means which checks toensure that a predetermined number of data elements and characteristicshave been entered and rated so as to provide useful results.
 27. Anapparatus as claimed in claim 26 wherein a window appears ifinsufficient information is provided informing a user that moreinformation must be inputted.
 28. An apparatus as claimed in claim 1wherein the apparatus includes means for comparing the results of ananalysis with the results of a previous analysis.
 29. An apparatus asclaimed in claim 1 wherein data is archived as a user modifies data. 30.An apparatus as claimed in claim 1 wherein the apparatus is a dataprocessing means having a visual display for communicating informationto a user.
 31. An apparatus as claimed in claim 1 including a keyboardand or mouse for data entry or selection.
 32. A computer controlledmethod of analysing data comprising: inputting a plurality of dataelements into a data processor or selecting a plurality of elements.stored in memory of the data processor; actuating the data processor togroup the data elements into a singleton and a pair of elements;inputting or selecting a common characteristic between the pair ofelements and a difference characteristic between the pair of elementsand the singleton; inputting ranking information to the data processorto rank the data elements in relation to the characteristics; processingrankings between elements and/or characteristics in the data processorto determine elements and/or characteristics having selected levels ofcorrelation; inputting a correlation threshold; and displaying thoseelements or characteristics having a degree of correlation above orbelow the correlation threshold.
 33. A method as claimed in claim 32wherein the data processor automatically groups the data elements uponuser actuation.
 34. A method as claimed in claim 32 wherein the dataprocessor groups the data elements according to user selection.
 35. Amethod as claimed in claim 34 wherein the data processor processesrankings only if a required number of data elements and characteristicshave been entered and rated.
 36. A method as claimed in claim 34 whereinthe data processor compares the result of an analysis with results of aprevious analysis.
 37. A method as claimed in claim 32 wherein the dataprocessor displays a qualifier in association with the grouped elementsand a user is required to input characteristics in relation to thequalifier.
 38. A method as claimed in claim 37 wherein a user inputs aqualifier to the data processor.
 39. A method as claimed in claim 37wherein the qualifier is selected by a user from a plurality ofqualifiers stored in said data processor.
 40. A method as claimed inclaim 32 wherein a plurality of characteristics are input for aplurality of different element groupings.
 41. A method as claimed inclaim 32 wherein the data processor prompts a user to input ageneralization of a characteristic upon user actuation.
 42. A method asclaimed in claim 32 wherein the data processor prompts a user to morespecifically define a characteristic upon user actuation.
 43. A methodas claimed in claim 32 wherein elements are ranked in relation to acharacteristic by a user entering or selecting a value within ameasurement range to the data processor.
 44. A method as claimed inclaim 43 wherein the data processor processes the values into a matrixin which the data elements define one axis and the characteristicsdefine the other axis.
 45. A method as claimed in claim 44 wherein thedata processor compares the values for each column of elements and/orrow of characteristics to find the closest correlation, forms a newcolumn and/or row combining the most closely correlated rows and/orcolumns respectively and continues to compare rows and/or columns inthis manner until only two rows and/or columns are left.
 46. A method asclaimed in claim 45 wherein the data processor displays the correlationbetween columns and rows of the matrix, representing the correlationbetween elements and characteristics.
 47. A method as claimed in claim46 wherein the data processor shows the correlation between rows andcolumns dendritically.
 48. A method as claimed in claim 45 wherein therows and/or columns are arranged by iteratively comparing the two rowsand/or columns forming a selected row and/or column and placing the mostrecently formed row and/or column to the right of the selected rowand/or column and the other row and/or to the left.
 49. A method asclaimed in claim 32 wherein the data processor prompts a user to reviewelements or characteristics having a defined degree of correlation. 50.A method as claimed in claim 49 wherein the data processor displays setsof two closely correlated elements or characteristics and combines themon user selection.
 51. A method as claimed in claim 32 wherein the dataprocessor processes rankings only if a required number of data elementsand characteristics have been entered and rated.
 52. A method as claimedin claim 32 wherein the data processor compares the result of ananalysis with results of a previous analysis.
 53. An apparatus foranalysing data comprising: means for inputting or selecting a pluralityof data elements according to user command; means for grouping dataelements into groups; means for communicating the elements of the groupsto a user and prompting the user to input common and differentcharacteristics in relation to a defined qualifier; means for inputtingor selecting characteristics within and/or between data element groupsaccording to user command; means responsive to user command for rankingdata elements in relation to selected characteristics; and means forcomparing rankings between elements and/or characteristics and fordetermining the elements and/or characteristics having selected degreesof correlation.
 54. An apparatus for analysing data comprising: meansfor inputting or selecting a plurality of data elements according touser command; means for grouping data elements into groups; means forcommunicating the elements of the groups to a user; means for inputtingor selecting characteristics within and/or between data element groupsaccording to user command; means responsive to user command for rankingdata elements in relation to selected characteristics; means forcomparing rankings between elements and/or characteristics and fordetermining the elements and/or characteristics having selected degreesof correlation; and means which, upon user selection, prompts a user togeneralize or to more specifically to define a characteristic and storea new characteristic input by the user.